International Journal of Computer
Trends and Technology

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Volume 4 | Issue 3 | Year 2013 | Article Id. IJCTT-V4I3P132 | DOI : https://doi.org/10.14445/22312803/IJCTT-V4I3P132

An Efficient Classification Approach for the XML Documents

Navya sree.Yarramsetti, G.Siva Nageswara Rao

Citation :

Navya sree.Yarramsetti, G.Siva Nageswara Rao, "An Efficient Classification Approach for the XML Documents," International Journal of Computer Trends and Technology (IJCTT), vol. 4, no. 3, pp. 362-366, 2013. Crossref, https://doi.org/10.14445/22312803/IJCTT-V4I3P132

Abstract

Extensible Markup Language (XML) has been used as standard format for a data representation over the internet. An XML document is usually organized by a set of textual data according to a predefined logical structure. Due to the presence of inherent structure in the XML documents, conventional text classification methods cannot be used to classify XML documents directly. In this paper, we propose the learning issues with XML documents from three major research areas. First, a knowledge representation method, which is based on typed higher order logic formalism. Here, the main focus is how to represent an XML document using higher order logic terms where both its contents and structures are captured. Second-symbolic machine learning. Here, a new decision-tree learning algorithm determined by precision/recall breakeven point (PRDT) for the XML document classification problem. Precision/recall heuristic is considered in xml document classification is that the xml documents have strong connections with text documents.  Finally, we had a semi-supervised learning algorithm which is based on the PRDT algorithm and the co-training framework. By producing comprehensible theories, the tentative results exhibit that our framework is capable to attain good performance in both the machine learning techniques. 

Keywords

precision/recall, Co-training, machine learning, knowledge representation, semi-supervised learning.

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